README.md

metadata

language:
  - he
license: mit
task_categories:
  - automatic-speech-recognition
tags:
  - speech-recognition
  - lyrics
  - hebrew
  - music
pretty_name: Caspi STT Benchmark
size_categories:
  - n<1K

Caspi STT Benchmark

Hebrew speech-to-text (STT) evaluation dataset built from Mati Caspi songs: YouTube audio plus reference lyrics, packaged for Hugging Face.

Dataset description

• Audio: 16 kHz mono WAV segments (one row per track or segment).
• Text: Reference transcript (lyrics or song title when lyrics are missing).
• Metadata: id, youtube_id, title, song_name.

Intended for STT benchmarking: compare model transcriptions to text (e.g. WER/CER).

How to use

from datasets import load_dataset, Audio

ds = load_dataset("ozlabs/caspi", split="train")
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))

# Example row
ex = ds[0]
# ex["audio"] → decoded array; ex["text"] → reference transcript

Source

• Audio: extracted from YouTube (playlists) at 16 kHz mono.
• Lyrics: provided manually or via Shazam;

License

MIT (or as specified in the repo). Audio and lyrics are used for research/evaluation; respect YouTube and lyric providers’ terms.